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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Recursive Feature Elimination (RFE): Working, Advantages & Examples

a
avcontentteam Last Updated : 09 Sep, 2024
6 min read

How can we sift through many variables to identify the most influential factors for accurate predictions in machine learning? Recursive Feature Elimination offers a compelling solution, and RFE iteratively removes less important features, creating a subset that maximizes predictive accuracy. By leveraging a machine learning algorithm and an importance-ranking metric, RFE evaluates each feature’s impact on model performance. Join us on this enlightening journey into Recursive Feature Elimination and unlock the potential to unleash accurate and robust predictive models.

Overview:

  • Recursive Feature Elimination (RFE) is a method to iteratively remove less significant features, focusing on those that enhance predictive accuracy.
  • RFE ranks feature importance, removes the least important, and rebuilds the model until a desired feature subset is obtained.
  • Unlike filtering and wrapper methods, RFE considers feature interactions. It offers robust performance in complex datasets but may be computationally demanding.
  • Implementing RFE involves scaling data and using tools like scikit-learn’s RFE or RFECV, with examples provided in Python.

Table of contents

What is Recursive Feature Elimination?

Recursive Feature Elimination is a feature selection method to identify a dataset’s key features. The process involves developing a model with the remaining features after repeatedly removing the least significant parts until the desired number of features is obtained. Although Recursive Feature Elimination (RFE) can be used with any supervised learning method, Support Vector Machines (SVM) are the most popular pairing.

How Recursive Feature Elimination Works?

RFE Steps | Recursive Feature Elimination

Recursive Feature Elimination algorithm works in the following steps:

  1. Rank the importance of all features using the chosen RFE machine learning algorithm.
  2. Eliminate the least important feature.
  3. Build a model using the remaining features.
  4. Repeat steps 1-3 until the desired number of features is reached.

Comparison of RFE With Other Feature Selection Methods

Compared to other feature selection methods, RFE has the advantage of considering interactions between features and is suitable for complex datasets.

Many methods are available for selecting RFE features, each with its own pros and cons. It’s important to understand each method’s benefits and downsides and choose the one that best addresses the issue.

Few Other Feature Selection Methods:

Filtering Method

A common method of Recursive feature selection is the filtering method. This method evaluates each feature individually and selects the most meaningful features based on statistical measures such as correlation and mutual information. Filtering techniques are quick and easy to implement but may not consider interactions between features and may not be effective with high-dimensional datasets.

Wrapper Method

Another common method is a wrapper method that uses a learning algorithm that evaluates the usefulness of each subset of functions. Wrapper methods are more computationally expensive than filter methods but can consider the interactions between features and may be more effective in high-dimensional datasets. However, they are more prone to overfitting and may be sensitive to the choice of learning algorithm.

Also Read: Feature Selection using Wrapper methods in Python

Principal Component Analysis (PCA)

Another method often compared to Recursive Feature Elimination is principal component analysis (PCA). It transforms features into a low-dimensional space that captures the most important information. PCA is an effective way to reduce the dimensionality of datasets and remove redundant features. Still, it may not preserve the interpretability of the original features and may not be suitable for non-linear relationships between features. There is nature.

Compared to filter and wrapper methods, RFE has the advantage of considering both features’ relevance, redundancy, and interactions. By recursively removing the least important features, RFE can effectively reduce the dataset’s dimensionality while preserving the most informative features. However, RFE can be computationally intensive and unsuitable for large datasets.

Therefore, the choice of feature selection method depends on the dataset’s specific properties and the analysis’s goals. Recursive Feature Elimination is a powerful and versatile method that effectively handles high-dimensional datasets and interactions between features. However, it is only suitable for some datasets.

Implementation of Recursive Feature Elimination

To implement RFE, we need to prepare the data by scaling and normalizing it. Then, we can use sci-kit-learn’s RFE or RFECV (recursive feature elimination with cross-validation) classes to select the features. Here are some examples of using RFE Python with scikit-learn, caret, and other libraries:

Using scikit-learn’s RFE:

from sklearn.feature_selection import RFE

from sklearn.svm import SVR

from sklearn.datasets import fetch_california_housing

data = fetch_california_housing()

X, y = data.data, data.target

estimator = SVR(kernel="linear")

selector = RFE(estimator, n_features_to_select=5, step=1)

selector.fit(X, y)

print(selector.support_)

print(selector.ranking_)

Best Practices for RFE

For best results with Recursive Feature Elimination, you should consider the following best practices:

Choose the Appropriate Number of Features

It helps to balance model power and complexity by choosing an appropriate number of features. Try different numbers of features and evaluate the model’s performance.  

Sets the Number of Cross-Validation Folds

Cross-validation helps reduce overfitting and improve model generalization. You should set the number of cross-validation folds based on the size of your dataset and the number of features.

High Dimensional Processing

Recursive Feature Elimination can handle high-dimensional datasets but can be computationally expensive. Dimensionality reduction techniques such as PCA and LDA can be used before applying RFE.

Dealing with Multicollinearity

RFE can handle multicollinearity but may not be the best approach. Other techniques, such as PCA and regularisation, can also deal with multicollinearity. 

Avoid Overfitting or Underfitting

RFE can reduce the risk of overfitting by choosing the most important features. However, removing important features can also lead to underfitting. Evaluate the overall performance of the models inside the holdout set to ensure that the models are well-rested and well-fitted.

Advantages and Limitations of Recursive Feature Elimination (RFE)

RFE has several advantages over other feature selection methods:

  • Can handle high-dimensional datasets and identify the most important features.
  • Can handle interactions between features and is suitable for complex datasets.
  • Can be used with any supervised learning algorithm.

However, RFE also has some limitations:

  • Can be computationally expensive for large datasets.
  • May not be the best approach for datasets with many correlated features.
  • May not work well with noisy or irrelevant features.

Therefore, evaluating the dataset and selecting an appropriate feature selection method based on the dataset’s characteristics is important.

Real-World Applications of Recursive Feature Elimination

Recursive Feature Elimination success stories and use cases demonstrate the effectiveness and efficiency of RFE in solving real-world problems. For example:

  • Bioinformatics: RFE selects genes for cancer diagnosis and prognosis. By choosing the most meaningful genes, RFE can help improve the accuracy of cancer diagnosis and provide patients with personalized treatment plans. 
  • Image Processing: RFE has been used to select image classification and recognition features. By choosing the most informative features, RFE can help improve the accuracy of image classification and recognition systems in various applications, such as autonomous driving and security systems.
  • Finance: RFE has been used in finance to select credit scoring and fraud detection features. By selecting the most relevant features, RFE can help improve the accuracy of credit scoring models and detect fraudulent activities in financial transactions.
  • Marketing: RFE has been used to select customer segmentation and recommendation system features. By selecting the most relevant features, RFE can help identify customer segments and provide personalized recommendations, improving customer satisfaction and increasing sales.

Conclusion

Recursive feature elimination (RFE) is a powerful function selection method that could perceive a data set’s most crucial capabilities. Recursively put off much less crucial functions and use the final capabilities to construct the model until you reach the desired variety of functions. It is possible to use a supervised learning algorithm with SVM. To get the best results with RFE, we need to follow best practices and consider the dataset’s characteristics. RFE has been used in various industries and domains and has demonstrated its effectiveness in solving real-world problems.

To deepen your understanding of RFE and other advanced techniques in data analysis, consider enrolling in our BlackBelt Program. This comprehensive program provides in-depth training, hands-on experience, and practical knowledge to sharpen your skills and become a proficient data scientist. Sign-up today!

Frequently Asked Questions

Q1. What is recursive feature elimination in R?

A. Recursive Feature Elimination (RFE) in R is a feature selection technique that iteratively eliminates less important features based on an algorithm and importance-ranking metric to identify the most relevant subset of features.

Q2. What is recursive feature elimination in logistic regression?

A. Recursive Feature Elimination in logistic regression selects the most significant features for the logistic regression model, improving interpretability and predictive accuracy.

Q3. What is the RFE method used for?

RFE is used for feature selection in various machine learning algorithms to improve model performance, reduce dimensionality, and enhance interpretability.

Q4. What is recursive feature elimination for classification in Python?

Recursive Feature Elimination for classification in Python iteratively removes less relevant features to improve accuracy, reduce overfitting, and enhance interpretability in classification tasks using algorithms like logistic regression, decision trees, random forests, and support vector machines.

Artificial Intelligencedata sciencemachine learningRecursive Feature EliminationRFErfe feature selectionrfe in machine learning
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